Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods

Prager, Raphael Patrick; Seiler, Moritz Vinzent; Trautmann, Heike; Kerschke, Pascal


Zusammenfassung

In recent years, feature-based automated algorithm selection using exploratory landscape analysis has demonstrated its great potential in single-objective continuous black-box optimization. However, feature computation is problem-specific and can be costly in terms of computational resources. This paper investigates feature-free approaches that rely on state-of-the-art deep learning techniques operating on either images or point clouds. We show that point-cloud-based strategies, in particular, are highly competitive and also substantially reduce the size of the required solver portfolio. Moreover, we highlight the effect and importance of cost-sensitive learning in automated algorithm selection models.

Schlüsselwörter
Automated Algorithm Selection; Exploratory Landscape Analysis; Deep Learning; Continuous Optimization



Publikationstyp
Forschungsartikel in Sammelband (Konferenz)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2022

Konferenz
International Conference on Parallel Problem Solving from Nature

Konferenzort
Dortmund

Buchtitel
Parallel Problem Solving from Nature -- PPSN XVII

Herausgeber
Rudolph, Günter; Kononova, Anna V.; Aguirre, Hernán; Kerschke, Pascal; Ochoa, Gabriela; Tušar, Tea

Erste Seite
3

Letzte Seite
17

Verlag
Springer International Publishing

Ort
Cham

Sprache
Englisch

ISBN
978-3-031-14714-2

DOI

Gesamter Text